Antidepressant Use in the Absence of Common Mental Disorders in the General Population
Bibliographic record
Abstract
OBJECTIVE: To examine the prevalence of antidepressant use in the absence of lifetime mental disorders and to examine sociodemographic correlates, indicators of need (hospitalization, suicidal behavior, perceived need, subthreshold disorders, disability, traumatic events), and antidepressant characteristics of such use. METHOD: Data came from the Collaborative Psychiatric Epidemiologic Surveys (N = 20,013), a nationally representative cross-sectional sample of community-dwelling adults in the United States. Sociodemographic correlates and indicators of need were examined as predictors of past-year use of antidepressants in the absence of a lifetime DSM-IV diagnosis as assessed by the World Mental Health Composite Diagnostic Interview. The surveys were conducted between 2001 and 2003. RESULTS: Among individuals who took an antidepressant in the past year (n = 1,441), 396 (26.3%) did not meet criteria for any lifetime diagnosis assessed. Respondents taking antidepressants in the absence of a lifetime diagnosis tended to be older, white, and female. All indicators of need except past-year suicidal behavior were significant predictors (adjusted odds ratios ranging from 2.12 to 14.22, P < .001), with 89% of individuals taking antidepressants in the absence of a lifetime diagnosis endorsing at least 1 indicator of need. Individuals taking antidepressants in the absence of a DSM-IV disorder were more likely to have been prescribed these medications by family physicians or other doctors compared to psychiatrists. CONCLUSIONS: These results suggest that antidepressant use among individuals without psychiatric diagnoses is common in the United States and is typically motivated by other indicators of need. These findings have important implications for the delivery of medical and psychiatric care and psychiatric nosology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".